Senior AI-Augmented Software Engineer

Flexera

1d•Remote

About The Position

Flexera is building a software engineering model in which a small, autonomous team combines strong engineering fundamentals with AI coding agents to deliver useful software quickly, safely, and reliably. The Senior AI-Augmented Software Engineer works closely with a Principal Engineer and another software engineer to design, build, test, deploy, and improve software used by Flexera and its customers. AI coding tools, including Claude Code and OpenAI Codex, are integral to the role. The engineer uses these tools to investigate code, implement and test solutions, and accelerate learning, while evaluating generated code for correctness and ensuring it is understandable and maintainable. The role offers hands-on experience building production software, ownership of meaningful features, technical mentorship, and opportunities to develop engineering judgment. The engineer contributes across the software development lifecycle, takes responsibility for assigned features through production readiness, collaborates with teammates, and progressively takes on more complex work and independent decisions.

Requirements

Foundational programming experience through professional work, internships, academic projects, or substantial personal projects.
Working knowledge of at least one programming language relevant to the company’s technology stack.
Understanding of core software engineering concepts, including data structures, control flow, APIs, debugging, and version control.
Familiarity with Git and collaborative development practices.
Ability to break problems into smaller steps, investigate unfamiliar concepts, and learn new technologies independently.
Interest in using AI tools to improve software development and willingness to learn effective prompting, context management, and code validation.
Strong analytical thinking and attention to detail.
Clear communication skills and ability to ask for help when needed.
Sense of ownership, intellectual curiosity, and commitment to delivering reliable software.

Nice To Haves

Hands-on experience with AI coding tools such as Claude Code, OpenAI Codex, GitHub Copilot, or comparable agentic development tools.
Experience building applications, APIs, integrations, automation scripts, or other working software.
Familiarity with automated testing, CI/CD, cloud platforms, containers, or relational databases.
Experience reading, debugging, and extending an existing codebase rather than working exclusively on greenfield projects.
Examples of using AI to build a project, solve a difficult technical problem, or improve an existing implementation.
Interest in learning multiple layers of the application stack and contributing beyond narrowly defined coding tasks.

Responsibilities

Implement product features, APIs, integrations, internal tools, and engineering improvements using the company’s technology stack.
Translate requirements and technical designs into small, well-defined implementation tasks.
Use AI coding agents to accelerate implementation, explore alternatives, generate tests, and improve existing code.
Contribute to requirements clarification, design, implementation, testing, deployment, and support.
Deliver working software in small increments and incorporate feedback quickly.
Own assigned features from initial understanding through production readiness.
Direct AI agents with clear objectives, repository context, constraints, and acceptance criteria; break larger tasks into manageable steps and coordinate multiple AI-assisted tasks when appropriate.
Review AI-generated code for correctness, readability, security, performance, and consistency with existing architecture; identify hallucinations, incorrect assumptions, unnecessary complexity, and defects.
Use AI to investigate unfamiliar codebases, understand errors, explain complex logic, and accelerate learning.
Capture reusable instructions, document discoveries, and share effective AI-assisted development techniques with teammates.
Write and maintain unit tests, integration tests, and other appropriate automated checks.
Reproduce bugs, identify root causes, and implement durable fixes.
Validate AI-generated code against requirements and expected behavior.
Participate in code reviews and respond constructively to feedback.
Use automated testing, linting, type checking, and CI/CD pipelines to detect problems early.
Follow security practices and avoid exposing credentials, sensitive information, or production data to unauthorized tools.
Maintain clear documentation and understandable code.
Communicate progress, risks, and blockers early; work with the team lead to clarify requirements and make technical decisions.
Collaborate with a teammate on implementation reviews, investigations, and knowledge sharing.
Ask questions, challenge questionable assumptions, and suggest simpler or better solutions.
Participate in technical discussions and contribute ideas to improve the product and development process.
Build foundations in software design, debugging, testing, security, and system architecture; learn to use AI without relying on code that cannot be explained or maintained.
Explore tools and techniques that improve software quality and delivery efficiency, and share lessons from implementations, experiments, and production issues.
Develop the skills to own increasingly complex systems and features independently.